R Notebook

library(tidyverse)
library(plotly)

l_df <- read_csv("levels-cleaned.csv")

Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
  .default = col_double(),
  timestamp = col_character(),
  company = col_character(),
  level = col_character(),
  title = col_character(),
  location = col_character(),
  tag = col_character(),
  gender = col_character(),
  city = col_character(),
  state = col_character(),
  month = col_character()
)
See spec(...) for full column specifications.

companyCompensation <- 
  l_df %>%
  group_by(year, company) %>%
  summarise(yearly_compensation = mean(totalyearlycompensation))

`summarise()` regrouping output by 'year' (override with `.groups` argument)

cityCompensation <- 
  l_df %>%
  group_by(year, location) %>%
  summarise(yearly_compensation = mean(totalyearlycompensation))

`summarise()` regrouping output by 'year' (override with `.groups` argument)

Yearly mean compensation for top 5 companies in 2017, 2018, 2019, 2020, 2021

companyCompensation2017 <- 
  companyCompensation %>%
  filter(year == 2017) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2018 <- 
  companyCompensation %>%
  filter(year == 2018) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2019 <- 
  companyCompensation %>%
  filter(year == 2019) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2020 <- 
  companyCompensation %>%
  filter(year == 2020) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2021 <- 
  companyCompensation %>%
  filter(year == 2021) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(companyCompensation2017, companyCompensation2018, companyCompensation2019, companyCompensation2020, companyCompensation2021, nrows = 5)

Yearly mean compensation for top 5 cities in 2017, 2018, 2019, 2020, 2021

cityCompensation2017 <- 
  cityCompensation %>%
  filter(year == 2017) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018 <- 
  cityCompensation %>%
  filter(year == 2018) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2019 <- 
  cityCompensation %>%
  filter(year == 2019) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2020 <- 
  cityCompensation %>%
  filter(year == 2020) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021 <- 
  cityCompensation %>%
  filter(year == 2021) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(cityCompensation2017, cityCompensation2018, cityCompensation2019, cityCompensation2020, cityCompensation2021, nrows = 5)

Yearly mean compensation for last 5 companies in 2017, 2018, 2019, 2020, 2021

companyCompensation2017 <- 
  companyCompensation %>%
  filter(year == 2017) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2018 <- 
  companyCompensation %>%
  filter(year == 2018) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2019 <- 
  companyCompensation %>%
  filter(year == 2019) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2020 <- 
  companyCompensation %>%
  filter(year == 2020) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

companyCompensation2021 <- 
  companyCompensation %>%
  filter(year == 2021) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~company,
    type = "bar",
    text = ~paste(company, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(companyCompensation2017, companyCompensation2018, companyCompensation2019, companyCompensation2020, companyCompensation2021, nrows = 5)

Yearly mean compensation for last 5 cities in 2017, 2018, 2019, 2020, 2021

cityCompensation2017 <- 
  cityCompensation %>%
  filter(year == 2017) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018 <- 
  cityCompensation %>%
  filter(year == 2018) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2019 <- 
  cityCompensation %>%
  filter(year == 2019) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2020 <- 
  cityCompensation %>%
  filter(year == 2020) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021 <- 
  cityCompensation %>%
  filter(year == 2021) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(cityCompensation2017, cityCompensation2018, cityCompensation2019, cityCompensation2020, cityCompensation2021, nrows = 5)

Making YOE Levels

YOE_Levels <- 
  l_df %>%
  mutate(YOE = case_when(between(yearsofexperience, 0, 2) ~ "0-2",
                         between(yearsofexperience, 2, 5) ~ "2-5",
                         between(yearsofexperience, 5, 10) ~ "5-10",
                         yearsofexperience >= 10 ~ "10+"))

cityCompensationYOE <-
  YOE_Levels %>%
  group_by(year, YOE, location) %>%
  summarise(yearly_compensation = mean(totalyearlycompensation))

`summarise()` regrouping output by 'year', 'YOE' (override with `.groups` argument)

2018 top 5 locations with experience plots

cityCompensation2018_01 <- 
  cityCompensationYOE %>%
  filter(YOE == "0-2") %>%
  filter(year == 2018) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018_15 <- 
  cityCompensationYOE %>%
  filter(YOE == "2-5") %>%
  filter(year == 2018) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018_510 <- 
  cityCompensationYOE %>%
  filter(YOE == "5-10") %>%
  filter(year == 2018) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018_10_ <- 
  cityCompensationYOE %>%
  filter(YOE == "10+") %>%
  filter(year == 2018) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(cityCompensation2018_01, cityCompensation2018_15, cityCompensation2018_510, cityCompensation2018_10_, nrows = 4)

2021 top 5 locations with experience plots

cityCompensation2021_01 <- 
  cityCompensationYOE %>%
  filter(YOE == "0-2") %>%
  filter(year == 2021) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021_15 <- 
  cityCompensationYOE %>%
  filter(YOE == "2-5") %>%
  filter(year == 2021) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021_510 <- 
  cityCompensationYOE %>%
  filter(YOE == "5-10") %>%
  filter(year == 2021) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021_10_ <- 
  cityCompensationYOE %>%
  filter(YOE == "10+") %>%
  filter(year == 2021) %>%
  arrange(desc(yearly_compensation)) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(cityCompensation2021_01, cityCompensation2021_15, cityCompensation2021_510, cityCompensation2021_10_, nrows = 4)

2018 last 5 locations with experience plots

cityCompensation2018_01 <- 
  cityCompensationYOE %>%
  filter(YOE == "0-2") %>%
  filter(year == 2018) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018_15 <- 
  cityCompensationYOE %>%
  filter(YOE == "2-5") %>%
  filter(year == 2018) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018_510 <- 
  cityCompensationYOE %>%
  filter(YOE == "5-10") %>%
  filter(year == 2018) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2018_10_ <- 
  cityCompensationYOE %>%
  filter(YOE == "10+") %>%
  filter(year == 2018) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(cityCompensation2018_01, cityCompensation2018_15, cityCompensation2018_510, cityCompensation2018_10_, nrows = 4)

2021 last 5 locations with experience plots

cityCompensation2021_01 <- 
  cityCompensationYOE %>%
  filter(YOE == "0-2") %>%
  filter(year == 2021) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021_15 <- 
  cityCompensationYOE %>%
  filter(YOE == "2-5") %>%
  filter(year == 2021) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021_510 <- 
  cityCompensationYOE %>%
  filter(YOE == "5-10") %>%
  filter(year == 2021) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

cityCompensation2021_10_ <- 
  cityCompensationYOE %>%
  filter(YOE == "10+") %>%
  filter(year == 2021) %>%
  arrange(yearly_compensation) %>%
  head(n = 5) %>%
  plot_ly(
    x = ~yearly_compensation,
    y = ~location,
    type = "bar",
    text = ~paste(location, ": $", round(yearly_compensation), sep = ""),
    hoverinfo = "text"
  ) %>%
  layout(showlegend = FALSE)

subplot(cityCompensation2021_01, cityCompensation2021_15, cityCompensation2021_510, cityCompensation2021_10_, nrows = 4)